At its core, Bayes’ Rule shows how you can optimize the chance of a correct decision by looking at previous data points that encompass the decision you are trying to make. In the case of hiring, this analysis would be more effective by looking at the metrics and data that shows who succeeds, looking at what makes someone successful in the position you are hiring for and reducing the impact of data that does not lead to good hiring decisions.

What most companies end up doing is using data as a filter but then hiring based on intuition. If you really want to make good decisions, you need to understand your intuition is only one (weak) data point and base the decision on Bayes’ Theorem, using past data to make the optimal decision.

What has worked for others

First, look at the position you are hiring for and identify the most successful people (at other companies or at your’s) in the field and “reverse engineer” their background. What experience(s) did they have before they were hired? What is their educational background (school, degree, extra curricular activities, etc.)? Using Bayes’ Rule, if you are hiring for a Director of Social Media and find that 90 percent of the top performing Directors of Social Media went to Texas A&M, then the chances of making a good hire from Texas Tech is already at less than 10 percent. Continue reading “Bayes’ Theorem Part 6: Making the best hiring choices”

Multiple data points are referred to as “continuous variables.” The values of a continuous variable are like densely packed points on a line, where each point corresponds to the value of a real number. The main advantage of working with continuous values is that we can usually describe a probability distribution with an equation, and because an equation is defined in terms of a few key parameters, it is said to provide a parametric description of a probability distribution.

To make the above relevant to you, think of yourself as a VC. You are looking at a potential investment. You start by looking at how similar investments over the last two years performed; this return on investment represents the points on the line. The parameters could be the space the business occupied, management team and level of investment. Rather than potential investments, to keep the analysis simple I will use a coin flip as an example. Continue reading “Bayes’ Theorem Part 2: How to use Bayes’ Rule when you have multiple prior data points”

Making the right decision, in business and in life, is the most important thing you can do. Wrong decisions can haunt you your entire life while the right decision can mean making your company worth billions, years of happiness, etc. Imagine if Travis Kalanick, CEO of Uber, had decided to focus on connecting buses with passengers and not taxis, or if Trip Hawkins would have focused 3DO on creating software and not a hardware platform. Understanding Bayes’ Theorem (also known as Bayes’ Rule, two terms I will use interchangeably) increases the chance you use data the right way to make your decisions.

This post is the first in a series I will be writing on Bayes’ Rule. This post and most of the background I discuss is based on the best book I have found about Bayes’ Rule, A Tutorial to Bayesian Analysis by James Stone. Last year, I wrote several posts on Lifetime Value (LTV), given how crucial it is to the success of any business, from the newest technology to the oldest brick and mortar enterprise. This year, we will be tackling Bayes’ Theorem. As you will see in the next few posts, by understanding Bayes’ Theorem you can then make optimal decisions about what games or projects to green light, how to staff your company, what to invest in, which technology to use, who to sell your company to, what areas of your company need to be fixed/improved, etc. Bayes’ Theorem is the single most important rule for good decision-making, both in your professional and business life.

What is Bayes’ Theorem?

Bayes’ Theorem is a rigorous method for interpreting evidence in the context of previous experience or knowledge. Bayes’ Theorem transforms the probabilities that look useful (but are often not), into probabilities that are useful. It is important to note that it is not a matter of conjecture; by definition a theorem is a mathematical statement has been proven true. Denying Bayes’ Theorem is like denying the theory of relativity.

Get my book on LTV

Understanding the Predictable delves into the world of Customer Lifetime Value (LTV), a metric that shows how much each customer is worth to your business. By understanding this metric, you can predict how changes to your product will impact the value of each customer. You will also learn how to apply this simple yet powerful method of predictive analytics to optimize your marketing and user acquisition.

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Lloyd Melnick

This is Lloyd Melnick’s personal blog. I am EVP Casino at VGW, where I lead the Chumba Casino team. I am a serial builder of businesses (senior leadership on three exits worth over $700 million), successful in big (Disney, Stars Group, Zynga) and small companies (Merscom, Spooky Cool Labs) with over 20 years experience in the gaming and casino space.